Direct Metal Deposition (DMD) is an additive manufacturing technology utilizing a precisely controlled laser beam to melt powders onto a substrate to form products. DMD with a closed-loop control system has been successfully applied in complicated part prototyping, repairs and surface modifications [1]. DMD is a multi-parameter process where, laser power, traverse speed and powder feed rate are considered the most dominant parameters that determine the dimensional accuracy and mechanical properties of products. Other secondary important parameters include laser beam size, delivery and shielding gases, nozzle design, bead overlap, z increment, tool path design, and powder qualities. Any disturbance from the controlling parameters, environment, and pool itself (surface tension, flow-ability), may shift the process away from its stable point and result in defects in the produced parts.
Mazumder et al. proposed a closed-loop controlled DMD system, in which three photo-detectors were used to monitor the molten pool height [1, 2]. A control unit, where an OR logic function was operated on the three signals from photo-detectors, was used to trigger off the laser when the detected pool height was above the pre-set limits. This closed-loop control system proved to be successful in controlling the dimensional accuracy of the produced parts. POM Group Inc. in Auburn Hills has commercialized the system and installed the system on three different continents.
A dynamic model of the DMD process is essential for advanced model based closed-loop controller designs. Several theoretical and numerical models have been studied to give insight into the process [3-7]. However, because of limitations, complexities and extensive numerical operations of the simulations, these models are not practical for in-process control. Experimental-based modeling using system identification has been reported to identify the nonlinear input-output dynamic relationship between traverse velocity and deposition bead height [8]. However, significant deviations existed between the actual data and the model outputs. To overcome the difficulties of the system modeling, a fuzzy logic controller was implemented where only the fuzzy knowledge of the process was needed [9].
Current sensing and modeling efforts have been focused on cladding tracks and molten pools. Monitoring cladding tracks can directly provide dimensional information of depositions [8]. However, monitoring cladding tracks introduces inherent process delays which must be compensated for in the controller. On the other hand, sensing molten pools can provide online process information, which could enable real time process control without process delays [1]. Optical intensity [1] and infrared images [10] of molten pools have been successfully employed to control the cladding process. Pool temperature measurement and transient mathematical modeling of the process have been reported by Han et al [6, 7]. Pool temperature during the process can indicate microstructures and mechanical properties of the products. A constant temperature during processing reflects a near uniform deposition.